In [2]:
import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import plotly.express as px
pd.set_option('display.max_rows', None)
from plotly.subplots import make_subplots
import seaborn as sns
import datetime

#Plotly Libraris
import plotly.express as px

# Minmax scaler
from sklearn.preprocessing import MinMaxScaler

#itertools
import itertools

#dataframe display settings
pd.set_option('display.max_columns', 5000000)
pd.set_option('display.max_rows', 50000000)

#to suppress un-necessary warnings
import warnings  
warnings.filterwarnings('ignore')


#Package to flatten python lists
from pandas.core.common import flatten
In [3]:
covid_data = pd.read_csv('covid_19_data.csv')
covid_confirmed = pd.read_csv('time_series_covid_19_confirmed.csv')
covid_deaths = pd.read_csv('time_series_covid_19_deaths.csv')
covid_recovered = pd.read_csv('time_series_covid_19_recovered.csv')
In [4]:
covid_confirmed.describe()
Out[4]:
Lat Long 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 1/29/20 1/30/20 1/31/20 2/1/20 2/2/20 2/3/20 2/4/20 2/5/20 2/6/20 2/7/20 2/8/20 2/9/20 2/10/20 2/11/20 2/12/20 2/13/20 2/14/20 2/15/20 2/16/20 2/17/20 2/18/20 2/19/20 2/20/20 2/21/20 2/22/20 2/23/20 2/24/20 2/25/20 2/26/20 2/27/20 2/28/20 2/29/20 3/1/20 3/2/20 3/3/20 3/4/20 3/5/20 3/6/20 3/7/20 3/8/20 3/9/20 3/10/20 3/11/20 3/12/20 3/13/20 3/14/20 3/15/20 3/16/20 3/17/20 3/18/20 3/19/20 3/20/20 3/21/20 3/22/20 3/23/20 3/24/20 3/25/20 3/26/20 3/27/20 3/28/20 3/29/20 3/30/20 3/31/20 4/1/20 4/2/20 4/3/20 4/4/20 4/5/20 4/6/20 4/7/20 4/8/20 4/9/20 4/10/20 4/11/20 4/12/20 4/13/20 4/14/20 4/15/20 4/16/20 4/17/20 4/18/20 4/19/20 4/20/20 4/21/20 4/22/20 4/23/20 4/24/20 4/25/20 4/26/20 4/27/20 4/28/20 4/29/20 4/30/20 5/1/20 5/2/20 5/3/20 5/4/20 5/5/20 5/6/20 5/7/20 5/8/20 5/9/20 5/10/20 5/11/20 5/12/20 5/13/20 5/14/20 5/15/20 5/16/20 5/17/20 5/18/20 5/19/20 5/20/20 5/21/20 5/22/20 5/23/20 5/24/20 5/25/20 5/26/20 5/27/20 5/28/20 5/29/20 5/30/20 5/31/20 6/1/20 6/2/20 6/3/20 6/4/20 6/5/20 6/6/20 6/7/20 6/8/20 6/9/20 6/10/20 6/11/20 6/12/20 6/13/20 6/14/20 6/15/20 6/16/20 6/17/20 6/18/20 6/19/20 6/20/20 6/21/20 6/22/20 6/23/20 6/24/20 6/25/20 6/26/20 6/27/20 6/28/20 6/29/20 6/30/20 7/1/20 7/2/20 7/3/20 7/4/20 7/5/20 7/6/20 7/7/20 7/8/20 7/9/20 7/10/20 7/11/20 7/12/20 7/13/20 7/14/20 7/15/20 7/16/20 7/17/20 7/18/20 7/19/20 7/20/20 7/21/20 7/22/20 7/23/20 7/24/20 7/25/20 7/26/20 7/27/20 7/28/20 7/29/20 7/30/20 7/31/20 8/1/20 8/2/20 8/3/20 8/4/20 8/5/20 8/6/20 8/7/20 8/8/20 8/9/20 8/10/20 8/11/20 8/12/20 8/13/20 8/14/20 8/15/20 8/16/20 8/17/20 8/18/20 8/19/20 8/20/20 8/21/20 8/22/20 8/23/20 8/24/20 8/25/20 8/26/20 8/27/20 8/28/20 8/29/20 8/30/20 8/31/20 9/1/20 9/2/20 9/3/20 9/4/20 9/5/20 9/6/20 9/7/20 9/8/20 9/9/20 9/10/20 9/11/20 9/12/20 9/13/20 9/14/20 9/15/20 9/16/20 9/17/20 9/18/20 9/19/20 9/20/20 9/21/20 9/22/20 9/23/20 9/24/20 9/25/20 9/26/20 9/27/20 9/28/20 9/29/20 9/30/20 10/1/20 10/2/20 10/3/20 10/4/20 10/5/20 10/6/20 10/7/20 10/8/20 10/9/20 10/10/20 10/11/20 10/12/20 10/13/20 10/14/20 10/15/20 10/16/20 10/17/20 10/18/20 10/19/20 10/20/20 10/21/20 10/22/20 10/23/20 10/24/20 10/25/20 10/26/20 10/27/20 10/28/20 10/29/20 10/30/20 10/31/20 11/1/20 11/2/20 11/3/20 11/4/20 11/5/20 11/6/20 11/7/20 11/8/20 11/9/20 11/10/20 11/11/20 11/12/20 11/13/20 11/14/20 11/15/20 11/16/20 11/17/20 11/18/20 11/19/20 11/20/20 11/21/20 11/22/20 11/23/20 11/24/20 11/25/20 11/26/20 11/27/20 11/28/20 11/29/20 11/30/20 12/1/20 12/2/20 12/3/20 12/4/20 12/5/20 12/6/20 12/7/20 12/8/20 12/9/20 12/10/20 12/11/20 12/12/20 12/13/20 12/14/20 12/15/20 12/16/20 12/17/20 12/18/20 12/19/20 12/20/20 12/21/20 12/22/20 12/23/20 12/24/20 12/25/20 12/26/20 12/27/20 12/28/20 12/29/20 12/30/20 12/31/20 1/1/21 1/2/21 1/3/21 1/4/21 1/5/21 1/6/21 1/7/21 1/8/21 1/9/21 1/10/21 1/11/21 1/12/21 1/13/21 1/14/21 1/15/21 1/16/21 1/17/21 1/18/21 1/19/21 1/20/21 1/21/21 1/22/21 1/23/21 1/24/21 1/25/21 1/26/21 1/27/21 1/28/21 1/29/21 1/30/21 1/31/21 2/1/21 2/2/21 2/3/21 2/4/21 2/5/21 2/6/21 2/7/21 2/8/21 2/9/21 2/10/21 2/11/21 2/12/21 2/13/21 2/14/21 2/15/21 2/16/21 2/17/21 2/18/21 2/19/21 2/20/21 2/21/21 2/22/21 2/23/21 2/24/21 2/25/21 2/26/21 2/27/21 2/28/21 3/1/21 3/2/21 3/3/21 3/4/21 3/5/21 3/6/21 3/7/21 3/8/21 3/9/21 3/10/21 3/11/21 3/12/21 3/13/21 3/14/21 3/15/21 3/16/21 3/17/21 3/18/21 3/19/21 3/20/21 3/21/21 3/22/21 3/23/21 3/24/21 3/25/21 3/26/21 3/27/21 3/28/21 3/29/21 3/30/21 3/31/21 4/1/21 4/2/21 4/3/21 4/4/21 4/5/21 4/6/21 4/7/21 4/8/21 4/9/21 4/10/21 4/11/21 4/12/21 4/13/21 4/14/21 4/15/21 4/16/21 4/17/21 4/18/21 4/19/21 4/20/21 4/21/21 4/22/21 4/23/21 4/24/21 4/25/21 4/26/21 4/27/21 4/28/21 4/29/21 4/30/21 5/1/21 5/2/21 5/3/21 5/4/21 5/5/21 5/6/21 5/7/21 5/8/21 5/9/21 5/10/21 5/11/21 5/12/21 5/13/21 5/14/21 5/15/21 5/16/21 5/17/21 5/18/21 5/19/21 5/20/21 5/21/21 5/22/21 5/23/21 5/24/21 5/25/21 5/26/21 5/27/21 5/28/21 5/29/21
count 274.000000 274.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 276.000000 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02 2.760000e+02
mean 20.447559 22.328281 2.018116 2.373188 3.409420 5.192029 7.673913 10.605072 20.210145 22.344203 29.836957 35.967391 43.615942 60.822464 72.054348 86.586957 100.155797 111.605072 124.623188 134.528986 145.507246 154.960145 162.358696 163.873188 218.775362 242.423913 250.188406 258.097826 265.471014 272.289855 274.101449 276.130435 278.409420 284.789855 286.166667 288.210145 291.300725 294.840580 299.768116 304.789855 311.641304 320.268116 327.452899 336.851449 345.213768 355.398551 369.576087 384.413043 398.721014 413.836957 431.307971 459.065217 480.043478 532.159420 572.282609 613.112319 666.764493 724.528986 795.416667 893.420290 1004.923913 1120.833333 1247.119565 1402.318841 1551.518116 1736.304348 1964.119565 2199.775362 2443.543478 2658.170290 2894.742754 3173.166667 3473.195652 3774.137681 4076.887681 4288.347826 4551.362319 4816.416667 5069.427536 5371.521739 5686.789855 5996.695652 6265.836957 6699.518116 6955.829710 7260.217391 7539.615942 7885.742754 8204.572464 8484.427536 8761.981884 9037.409420 9311.862319 9606.235507 9911.485507 10216.181159 10514.221014 10773.597826 1.102792e+04 1.130161e+04 1.158100e+04 1.188351e+04 1.220372e+04 1.249067e+04 1.276739e+04 1.304887e+04 1.334054e+04 1.366673e+04 1.398813e+04 1.431523e+04 1.462401e+04 1.489566e+04 1.517331e+04 1.547943e+04 1.578733e+04 1.613600e+04 1.648285e+04 1.682502e+04 1.710771e+04 1.743078e+04 1.778141e+04 1.815054e+04 1.853693e+04 1.892251e+04 1.930224e+04 1.964358e+04 1.995778e+04 2.029824e+04 2.067056e+04 2.110275e+04 2.154080e+04 2.203542e+04 2.242096e+04 2.276876e+04 2.321066e+04 2.362461e+04 2.409904e+04 2.457449e+04 2.505904e+04 2.546616e+04 2.583752e+04 2.629064e+04 2.678015e+04 2.728022e+04 2.774676e+04 2.823694e+04 2.871623e+04 2.914977e+04 2.966457e+04 3.018539e+04 3.069649e+04 3.134835e+04 3.191882e+04 3.238150e+04 3.288643e+04 3.349082e+04 3.411616e+04 3.476246e+04 3.545666e+04 3.610206e+04 3.669580e+04 3.725865e+04 3.789105e+04 3.867948e+04 3.944015e+04 4.016980e+04 4.087201e+04 4.153869e+04 4.213280e+04 4.289861e+04 4.367454e+04 4.449638e+04 4.533940e+04 4.612376e+04 4.682040e+04 4.751404e+04 4.831907e+04 4.915772e+04 5.006827e+04 5.094777e+04 5.180349e+04 5.257680e+04 5.332722e+04 5.417389e+04 5.518791e+04 5.621274e+04 5.723112e+04 5.815161e+04 5.892348e+04 5.974424e+04 6.066013e+04 6.171711e+04 6.273197e+04 6.378539e+04 6.468516e+04 6.552209e+04 6.627019e+04 6.720476e+04 6.820684e+04 6.924018e+04 7.026279e+04 7.119945e+04 7.201352e+04 7.283246e+04 7.376569e+04 7.476803e+04 7.581592e+04 7.691645e+04 7.781395e+04 7.859101e+04 7.934366e+04 8.027937e+04 8.129238e+04 8.227989e+04 8.322629e+04 8.418372e+04 8.493064e+04 8.574683e+04 8.663089e+04 8.765912e+04 8.868773e+04 8.971536e+04 9.067113e+04 9.147173e+04 9.242424e+04 9.338411e+04 9.441130e+04 9.543093e+04 9.656867e+04 9.754690e+04 9.838192e+04 9.917134e+04 1.000489e+05 1.010808e+05 1.021673e+05 1.033317e+05 1.043701e+05 1.052495e+05 1.062051e+05 1.072394e+05 1.083428e+05 1.094837e+05 1.106645e+05 1.117229e+05 1.126364e+05 1.136367e+05 1.146676e+05 1.156679e+05 1.169470e+05 1.181458e+05 1.191909e+05 1.201017e+05 1.210248e+05 1.220490e+05 1.232309e+05 1.243842e+05 1.254634e+05 1.266665e+05 1.276136e+05 1.287042e+05 1.298873e+05 1.311590e+05 1.324682e+05 1.337758e+05 1.350696e+05 1.361091e+05 1.371663e+05 1.383230e+05 1.397083e+05 1.411828e+05 1.426748e+05 1.440250e+05 1.451719e+05 1.465749e+05 1.479843e+05 1.495962e+05 1.513090e+05 1.531090e+05 1.547640e+05 1.560707e+05 1.578330e+05 1.595368e+05 1.613914e+05 1.633857e+05 1.654547e+05 1.671821e+05 1.688574e+05 1.708977e+05 1.729040e+05 1.747575e+05 1.769189e+05 1.792554e+05 1.814180e+05 1.831715e+05 1.849887e+05 1.870154e+05 1.893654e+05 1.917189e+05 1.940814e+05 1.962371e+05 1.979543e+05 1.998907e+05 2.021053e+05 2.043774e+05 2.067455e+05 2.091685e+05 2.112973e+05 2.130654e+05 2.149687e+05 2.171073e+05 2.194125e+05 2.215229e+05 2.240126e+05 2.261359e+05 2.279061e+05 2.297490e+05 2.319667e+05 2.343269e+05 2.368426e+05 2.393182e+05 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std 25.189838 74.369096 26.781738 26.879567 33.464159 46.575328 65.089830 87.699030 215.201418 216.521511 298.377294 353.754834 435.313401 676.494833 817.788348 1007.884613 1187.714772 1335.148694 1506.165586 1635.472132 1787.715018 1913.867390 2012.441881 2012.780205 2904.064734 3276.799205 3387.689187 3503.968094 3612.651852 3714.479264 3735.499036 3760.204488 3773.510303 3859.001700 3859.042744 3871.327211 3901.385100 3925.695938 3950.861847 3970.967454 3998.322007 4034.863650 4049.462611 4061.612746 4072.844072 4086.702355 4103.621569 4120.687415 4136.850716 4156.134361 4171.787491 4204.715289 4245.631689 4307.641449 4388.697303 4481.847988 4595.234467 4729.881563 4917.529380 5198.458483 5558.888496 5978.468064 6438.583287 7059.158412 7692.047092 8515.267351 9616.751990 10796.263981 12051.896248 13215.677758 14560.609057 16156.590130 18061.272464 19980.433792 21937.530422 23713.968743 25484.640357 27337.924989 29153.805488 31090.393817 33251.336708 35320.515033 37102.821892 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6.247464e+06 6.278633e+06 6.302200e+06 6.329593e+06 6.363650e+06 6.399723e+06 6.447501e+06 6.488563e+06 6.522914e+06 6.557342e+06 6.596849e+06 6.635867e+06 6.681004e+06 6.730288e+06 6.772447e+06 6.810862e+06 6.862834e+06 6.902696e+06 6.941758e+06 6.988869e+06 7.037151e+06 7.081803e+06 7.119311e+06 7.152546e+06 7.195994e+06 7.235428e+06 7.281081e+06 7.336043e+06 7.384578e+06 7.420293e+06 7.459742e+06 7.504998e+06 7.556060e+06 7.614653e+06 7.671034e+06 7.725952e+06 7.771893e+06 7.813735e+06 7.865983e+06 7.925748e+06 7.990636e+06 8.059782e+06 8.116518e+06 8.165858e+06 8.233610e+06 8.295581e+06 8.358864e+06 8.435164e+06 8.517113e+06 8.599842e+06 8.661982e+06 8.729385e+06 8.806228e+06 8.885632e+06 8.976684e+06 9.075924e+06 9.165619e+06 9.270467e+06 9.355775e+06 9.482891e+06 9.587499e+06 9.716853e+06 9.844858e+06 9.972308e+06 1.008738e+07 1.020795e+07 1.034845e+07 1.049508e+07 1.065991e+07 1.084030e+07 1.100806e+07 1.114429e+07 1.130723e+07 1.147116e+07 1.164433e+07 1.183588e+07 1.203418e+07 1.221345e+07 1.236024e+07 1.253468e+07 1.271020e+07 1.289348e+07 1.300581e+07 1.321400e+07 1.336953e+07 1.350976e+07 1.367033e+07 1.385855e+07 1.406111e+07 1.428472e+07 1.451751e+07 1.473305e+07 1.491406e+07 1.510892e+07 1.533341e+07 1.555595e+07 1.578746e+07 1.602744e+07 1.624503e+07 1.643273e+07 1.662755e+07 1.683656e+07 1.708326e+07 1.732298e+07 1.757495e+07 1.776686e+07 1.795468e+07 1.815372e+07 1.835174e+07 1.858135e+07 1.877556e+07 1.887320e+07 1.909949e+07 1.925513e+07 1.942976e+07 1.963001e+07 1.986370e+07 2.009936e+07 2.025299e+07 2.055330e+07 2.076205e+07 2.094633e+07 2.118144e+07 2.143688e+07 2.171517e+07 2.201039e+07 2.227108e+07 2.248433e+07 2.269933e+07 2.292625e+07 2.315661e+07 2.339232e+07 2.363505e+07 2.383673e+07 2.401451e+07 2.415792e+07 2.433463e+07 2.451787e+07 2.471168e+07 2.490244e+07 2.507305e+07 2.520411e+07 2.535608e+07 2.550362e+07 2.565757e+07 2.582618e+07 2.599274e+07 2.613506e+07 2.624705e+07 2.638226e+07 2.649759e+07 2.661923e+07 2.674320e+07 2.687760e+07 2.698159e+07 2.707124e+07 2.716155e+07 2.725718e+07 2.735236e+07 2.745812e+07 2.755776e+07 2.764488e+07 2.770990e+07 2.776409e+07 2.782681e+07 2.789692e+07 2.796685e+07 2.804614e+07 2.811767e+07 2.817475e+07 2.823097e+07 2.830323e+07 2.837796e+07 2.845547e+07 2.853281e+07 2.859739e+07 2.864874e+07 2.870697e+07 2.876403e+07 2.883123e+07 2.889928e+07 2.896573e+07 2.902393e+07 2.906494e+07 2.910997e+07 2.916762e+07 2.922554e+07 2.928801e+07 2.934953e+07 2.940246e+07 2.944069e+07 2.949735e+07 2.955131e+07 2.961044e+07 2.967098e+07 2.973261e+07 2.978799e+07 2.982175e+07 2.987335e+07 2.992695e+07 3.001391e+07 3.008138e+07 3.015870e+07 3.022140e+07 3.026449e+07 3.033392e+07 3.039517e+07 3.046221e+07 3.054126e+07 3.061109e+07 3.067415e+07 3.070912e+07 3.078680e+07 3.084735e+07 3.092239e+07 3.100226e+07 3.108496e+07 3.115150e+07 3.119788e+07 3.126811e+07 3.134598e+07 3.142136e+07 3.149565e+07 3.157564e+07 3.162801e+07 3.167003e+07 3.173796e+07 3.179924e+07 3.186209e+07 3.192935e+07 3.199175e+07 3.204511e+07 3.207718e+07 3.212487e+07 3.217572e+07 3.223085e+07 3.228905e+07 3.234697e+07 3.239227e+07 3.242164e+07 3.247220e+07 3.251293e+07 3.255767e+07 3.260518e+07 3.265247e+07 3.268696e+07 3.270836e+07 3.274526e+07 3.277891e+07 3.281478e+07 3.285287e+07 3.289517e+07 3.292398e+07 3.294085e+07 3.296951e+07 3.299733e+07 3.302662e+07 3.305676e+07 3.308511e+07 3.310488e+07 3.311774e+07 3.314366e+07 3.316642e+07 3.319047e+07 3.321800e+07 3.323996e+07 3.325194e+07
In [5]:
NAN = [(c, covid_data[c].isna().mean()*100) for c in covid_data]
NAN = pd.DataFrame(NAN, columns=["column_name", "percentage"])
NAN
Out[5]:
column_name percentage
0 SNo 0.000000
1 ObservationDate 0.000000
2 Province/State 25.487144
3 Country/Region 0.000000
4 Last Update 0.000000
5 Confirmed 0.000000
6 Deaths 0.000000
7 Recovered 0.000000

34 % of Province/State are unknown we will fill nan values with Unknown. 0

In [6]:
covid_data["Province/State"]= covid_data["Province/State"].fillna('Unknown')

Change Data Type for "Confirmed","Deaths" and "Recovered" columns to int

In [7]:
covid_data[["Confirmed","Deaths","Recovered"]] =covid_data[["Confirmed","Deaths","Recovered"]].astype(int)

Replacing "Mainland China" with "China"

In [8]:
covid_data['Country/Region'] = covid_data['Country/Region'].replace('Mainland China', 'China')
  • Creating new feature "Active_case"
  • Active_case = Confirmed - Deaths - Recovered
In [9]:
covid_data['Active_case'] = covid_data['Confirmed'] - covid_data['Deaths'] - covid_data['Recovered']
In [10]:
covid_data.head()
Out[10]:
SNo ObservationDate Province/State Country/Region Last Update Confirmed Deaths Recovered Active_case
0 1 01/22/2020 Anhui China 1/22/2020 17:00 1 0 0 1
1 2 01/22/2020 Beijing China 1/22/2020 17:00 14 0 0 14
2 3 01/22/2020 Chongqing China 1/22/2020 17:00 6 0 0 6
3 4 01/22/2020 Fujian China 1/22/2020 17:00 1 0 0 1
4 5 01/22/2020 Gansu China 1/22/2020 17:00 0 0 0 0
In [11]:
Data = covid_data[covid_data['ObservationDate'] == max(covid_data['ObservationDate'])].reset_index()

Time series stats¶

In [12]:
base_stats = pd.DataFrame(columns=['Dates','Confirmed','Deaths','Recovered','Active'])
base_stats['Dates'] = covid_confirmed.columns[4:]

base_stats['Confirmed'] = base_stats['Dates'].apply(lambda x: covid_confirmed[x].sum())
base_stats['Deaths'] = base_stats['Dates'].apply(lambda x: covid_deaths[x].sum())
base_stats['Recovered'] = base_stats['Dates'].apply(lambda x: covid_recovered[x].sum())
base_stats.reset_index(drop=False, inplace=True)
base_stats['Active'] = base_stats['index'].apply(lambda x: (base_stats['Confirmed'][x]-(base_stats['Deaths'][x]+base_stats['Recovered'][x])))
base_stats.head()
Out[12]:
index Dates Confirmed Deaths Recovered Active
0 0 1/22/20 557 17 30 510
1 1 1/23/20 655 18 32 605
2 2 1/24/20 941 26 39 876
3 3 1/25/20 1433 42 42 1349
4 4 1/26/20 2118 56 56 2006
In [13]:
latest_stats_fig = go.Figure()
latest_stats_fig.add_trace(go.Treemap(labels = ['Confirmed','Active','Recovered','Deaths'],
                                     parents = ['','Confirmed','Confirmed','Confirmed'],
                                     values = [base_stats['Confirmed'].sum(), base_stats['Active'].sum(), base_stats['Recovered'].sum(), base_stats['Deaths'].sum()],
                                      branchvalues="total", marker_colors = ['#118ab2','#ef476f','#06d6a0','#073b4c'],
                                      textinfo = "label+text+value",
                                      outsidetextfont = {"size": 30, "color": "darkblue"},
                                      marker = {"line": {"width": 2}},
                                        pathbar = {"visible": False}
                                     ))
latest_stats_fig.update_layout(#width=1000, 
                               height=300)
latest_stats_fig.show()

Cases over time around the world¶

In [14]:
base_stats_fig = go.Figure()

for column in base_stats.columns.to_list()[2:6]:
    color_dict = {
      "Confirmed": "#118ab2",
      "Active": "#ef476f",
      "Recovered": "#06d6a0",
      "Deaths": "#073b4c"
        }
    base_stats_fig.add_trace(
        go.Scatter(
            x = base_stats['Dates'],
            y = base_stats[column],
            name = column,
            line = dict(color=color_dict[column]),
            hovertemplate ='<br><b>Date</b>: %{x}'+'<br><i>Count</i>:'+'%{y}',
        )
    )
    
for column in base_stats.columns.to_list()[2:6]:
    color_dict = {
      "Confirmed": "#149ECC",
      "Active": "#F47C98",
      "Recovered": "#24F9C1",
      "Deaths": "#0C6583"
        }
    base_stats_fig.add_trace(
        go.Scatter(
            x = base_stats['Dates'],
            y = base_stats['index'].apply(lambda x: (base_stats[column][x-7:x].sum())/7 if x>7 else (base_stats[column][0:x].sum())/7),
            name = column+" 7-day Moving Avg.",
            line = dict(dash="dash", color=color_dict[column]), showlegend=False,
            hovertemplate = '<br><b>Date</b>: %{x}'+'<br><i>7-day moving avg.</i>: %{y}'
        )
    )
    
base_stats_fig.update_layout(
    updatemenus=[
        dict(
        buttons=list(
            [dict(label = 'All Cases',
                  method = 'update',
                  args = [{'visible': [True, True, True, True, True, True, True, True]},
                          {'title': 'All Cases',
                           'showlegend':True}]),
             dict(label = 'Confirmed',
                  method = 'update',
                  args = [{'visible': [True, False, False, False, True, False, False, False]},
                          {'title': 'Confirmed',
                           'showlegend':True}]),
             dict(label = 'Active',
                  method = 'update',
                  args = [{'visible': [False, False, False, True, False, False, False, True]},
                          {'title': 'Active',
                           'showlegend':True}]),
             dict(label = 'Recovered',
                  method = 'update',
                  args = [{'visible': [False, False, True, False, False, False, True, False]},
                          {'title': 'Recovered',
                           'showlegend':True}]),
             dict(label = 'Deaths',
                  method = 'update',
                  args = [{'visible': [False, True, False, False, False, True, False, False]},
                          {'title': 'Deaths',
                           'showlegend':True}]),
            ]),
             type = "dropdown",
             direction="down",
#             pad={"r": 10, "t": 40},
             showactive=True,
             x=0,
             xanchor="left",
             y=1.25,
             yanchor="top"
        ),
        dict(
        buttons=list(
            [dict(label = 'Linear Scale',
                  method = 'relayout',
                  args = [{'yaxis': {'type': 'linear'}},
                          {'title': 'All Cases',
                           'showlegend':True}]),
             dict(label = 'Log Scale',
                  method = 'relayout',
                  args = [{'yaxis': {'type': 'log'}},
                          {'title': 'Confirmed',
                           'showlegend':True}]),
            ]),
             type = "dropdown",
             direction="down",
#             pad={"r": 10, "t": 10},
             showactive=True,
             x=0,
             xanchor="left",
             y=1.36,
             yanchor="top"
        )
    ])

base_stats_fig.update_xaxes(showticklabels=False)
base_stats_fig.update_layout(
    #height=600, width=600, 
    title_text="Basic Statistics for Covid19", title_x=0.5, title_font_size=20,
                            legend=dict(orientation='h',yanchor='top',y=1.15,xanchor='right',x=1), paper_bgcolor="mintcream",
                            xaxis_title="Date", yaxis_title="Number of Cases")
base_stats_fig.show()
In [15]:
covid_data.head()
Out[15]:
SNo ObservationDate Province/State Country/Region Last Update Confirmed Deaths Recovered Active_case
0 1 01/22/2020 Anhui China 1/22/2020 17:00 1 0 0 1
1 2 01/22/2020 Beijing China 1/22/2020 17:00 14 0 0 14
2 3 01/22/2020 Chongqing China 1/22/2020 17:00 6 0 0 6
3 4 01/22/2020 Fujian China 1/22/2020 17:00 1 0 0 1
4 5 01/22/2020 Gansu China 1/22/2020 17:00 0 0 0 0

Increase in cases¶

In [16]:
daily_case_fig = make_subplots(rows=2, cols=2, vertical_spacing=0.05, horizontal_spacing=0.04, # shared_yaxes=True,
                           subplot_titles=('Confirmed','Active','Recovered','Deaths'),
                            x_title='Dates', y_title='# of Cases',)

daily_case_fig.add_trace(go.Bar(x=base_stats['Dates'], y=base_stats['index'].apply(lambda x: base_stats['Confirmed'][x]-base_stats['Confirmed'][x-1:x].sum()),
                              name='Confirmed',hovertemplate = '<br><b>Date</b>: %{x}'+'<br><i>Confirmed Count</i>: %{y}',
                                marker=dict(color='#118ab2')),row=1, col=1)
daily_case_fig.add_trace(go.Scatter(x=base_stats['Dates'], y=base_stats['index'].apply(lambda x: (base_stats['Confirmed'][x-7:x].sum()-base_stats['Confirmed'][x-8:x-1].sum())/7 if x>0 else 0),
                             name='7-day moving average', hovertemplate = '<br><b>Date</b>: %{x}'+'<br><i>7-day average</i>: %{y}', showlegend=False,
                                    line=dict(dash="dash", color='#149ECC')),row=1, col=1)

daily_case_fig.add_trace(go.Bar(x=base_stats['Dates'], y=base_stats['index'].apply(lambda x: base_stats['Active'][x]-base_stats['Active'][x-1:x].sum()), 
                             name='Active',hovertemplate = '<br><b>Date</b>: %{x}'+'<br><i>Active Count</i>: %{y}',
                               marker=dict(color='#ef476f')),row=1, col=2)
daily_case_fig.add_trace(go.Scatter(x=base_stats['Dates'], y=base_stats['index'].apply(lambda x: (base_stats['Active'][x-7:x].sum()-base_stats['Active'][x-8:x-1].sum())/7 if x>0 else 0),
                             name='7-day moving average', hovertemplate = '<br><b>Date</b>: %{x}'+'<br><i>7-day average</i>: %{y}', showlegend=False,
                                    line=dict(dash="dash", color='#F47C98')),row=1, col=2)

daily_case_fig.add_trace(go.Bar(x=base_stats['Dates'], y=base_stats['index'].apply(lambda x: base_stats['Recovered'][x]-base_stats['Recovered'][x-1:x].sum()), 
                              name='Recovered',hovertemplate = '<br><b>Date</b>: %{x}'+'<br><i>Recovered Count</i>: %{y}',
                               marker=dict(color='#06d6a0')),row=2, col=1)
daily_case_fig.add_trace(go.Scatter(x=base_stats['Dates'], y=base_stats['index'].apply(lambda x: (base_stats['Recovered'][x-7:x].sum()-base_stats['Recovered'][x-8:x-1].sum())/7 if x>0 else 0),
                             name='7-day moving average', hovertemplate = '<br><b>Date</b>: %{x}'+'<br><i>7-day average</i>: %{y}', showlegend=False,
                                    line=dict(dash="dash", color='#24F9C1')),row=2, col=1)

daily_case_fig.add_trace(go.Bar(x=base_stats['Dates'], y=base_stats['index'].apply(lambda x: base_stats['Deaths'][x]-base_stats['Deaths'][x-1:x].sum()), 
                              name='Deaths',hovertemplate = '<br><b>Date</b>: %{x}'+'<br><i>Death Count</i>: %{y}',
                               marker=dict(color='#073b4c')),row=2, col=2)
daily_case_fig.add_trace(go.Scatter(x=base_stats['Dates'], y=base_stats['index'].apply(lambda x: (base_stats['Deaths'][x-7:x].sum()-base_stats['Deaths'][x-8:x-1].sum())/7 if x>0 else 0),
                             name='7-day moving average', hovertemplate = '<br><b>Date</b>: %{x}'+'<br><i>7-day average</i>: %{y}', line=dict(dash="dash", color='#0C6583')),row=2, col=2)




daily_case_fig.update_xaxes(showticklabels=False)
daily_case_fig.update_layout(
    #height=600, width=1100, 
    title_text="Daily change in cases of Covid19", title_x=0.5, title_font_size=20,
                            legend=dict(orientation='h',yanchor='top',y=1.1,xanchor='right',x=1), paper_bgcolor="mintcream")


daily_case_fig.show()

Confirmed cases in each Country

In [17]:
Data_per_country = covid_data.groupby(["Country/Region"])["Confirmed","Active_case","Recovered","Deaths"].sum().reset_index().sort_values("Confirmed",ascending=False).reset_index(drop=True)
In [18]:
headerColor = 'grey'
rowEvenColor = 'lightgrey'
rowOddColor = 'white'

fig = go.Figure(data=[go.Table(
  header=dict(
    values=['<b>Country</b>','<b>Confirmed Cases</b>'],
    line_color='darkslategray',
    fill_color=headerColor,
    align=['left','center'],
      
    font=dict(color='white', size=12)
  ),
  cells=dict(
    values=[
      Data_per_country['Country/Region'],
      Data_per_country['Confirmed'],
      ],
    line_color='darkslategray',
    # 2-D list of colors for alternating rows
    fill_color = [[rowOddColor,rowEvenColor,rowOddColor, rowEvenColor,rowOddColor]*len(Data_per_country)],
    align = ['left', 'center'],
    font = dict(color = 'darkslategray', size = 11)
    ))
])
fig.update_layout(
    title='Confirmed Cases In Each Country',
)
fig.show()

Evolution of coronavirus over time.¶

In [19]:
data_per_country = covid_data.groupby(["Country/Region","ObservationDate"])[["Confirmed","Active_case","Recovered","Deaths"]].sum().reset_index().sort_values("ObservationDate",ascending=True).reset_index(drop=True)
In [20]:
fig = px.choropleth(data_per_country, locations=data_per_country['Country/Region'],
                    color=data_per_country['Active_case'],locationmode='country names', 
                    hover_name=data_per_country['Country/Region'], 
                    color_continuous_scale=px.colors.sequential.Tealgrn,
                    animation_frame="ObservationDate")
fig.update_layout(

    title='Evolution of active cases In Each Country',
    template='plotly_dark'
)
fig.show()
In [21]:
fig = px.choropleth(data_per_country, locations=data_per_country['Country/Region'],
                    color=data_per_country['Recovered'],locationmode='country names', 
                    hover_name=data_per_country['Country/Region'], 
                    color_continuous_scale=px.colors.sequential.deep,
                    animation_frame="ObservationDate")
fig.update_layout(
    title='Evolution of recovered cases In Each Country',
)
fig.show()
In [22]:
fig = px.choropleth(data_per_country, locations=data_per_country['Country/Region'],
                    color=data_per_country['Deaths'],locationmode='country names', 
                    hover_name=data_per_country['Country/Region'], 
                    color_continuous_scale=px.colors.sequential.Tealgrn,
                    animation_frame="ObservationDate")
fig.update_layout(
    title='Evolution of deaths In Each Country',
    template='plotly_dark'
)
fig.show()
In [23]:
fig = go.Figure(data=[go.Bar(
            x=Data_per_country['Country/Region'][0:10], y=Data_per_country['Confirmed'][0:10],
            text=Data_per_country['Confirmed'][0:10],
            textposition='auto',
            marker_color='pink',
            

        )])
fig.update_layout(
    title='Most 10 infected Countries',
    xaxis_title="Countries",
    yaxis_title="Confirmed Cases",
        template='plotly_white'

)
fig.show()

Recorvered cases in each Country¶

In [24]:
Recovered_per_country = covid_data.groupby(["Country/Region"])["Recovered"].sum().reset_index().sort_values("Recovered",ascending=False).reset_index(drop=True)
In [25]:
headerColor = 'grey'
rowEvenColor = 'lightgrey'
rowOddColor = 'white'

fig = go.Figure(data=[go.Table(
  header=dict(
    values=['<b>Country</b>','<b>Recovered Cases</b>'],
    line_color='darkslategray',
    fill_color=headerColor,
    align=['left','center'],
    font=dict(color='white', size=12)
  ),
  cells=dict(
    values=[
      Recovered_per_country['Country/Region'],
      Recovered_per_country['Recovered'],
      ],
    line_color='darkslategray',
    # 2-D list of colors for alternating rows
    fill_color = [[rowOddColor,rowEvenColor,rowOddColor, rowEvenColor,rowOddColor]*len(Data_per_country)],
    align = ['left', 'center'],
    font = dict(color = 'darkslategray', size = 11)
    ))
])
fig.update_layout(
    title='Recovered Cases In Each Country',
)
fig.show()
In [26]:
fig = px.pie(Recovered_per_country, values=Recovered_per_country['Recovered'], names=Recovered_per_country['Country/Region'],
             title='Recovered cases',
            )
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.update_layout(
    template='plotly_white'
)
fig.show()
In [27]:
fig = go.Figure(data=[go.Bar(
            x=Recovered_per_country['Country/Region'][0:10], y=Recovered_per_country['Recovered'][0:10],
            text=Recovered_per_country['Recovered'][0:10],
            textposition='auto',
            marker_color='lavender',

        )])
fig.update_layout(
    title='Most 10 infected Countries',
    xaxis_title="Countries",
    yaxis_title="Recovered Cases",
    template='plotly_white'
)
fig.show()

Active cases in each Country¶

In [28]:
Active_per_country = covid_data.groupby(["Country/Region"])["Active_case"].sum().reset_index().sort_values("Active_case",ascending=False).reset_index(drop=True)
In [29]:
headerColor = 'grey'
rowEvenColor = 'lightgrey'
rowOddColor = 'white'

fig = go.Figure(data=[go.Table(
  header=dict(
    values=['<b>Country</b>','<b>Active Cases</b>'],
    line_color='darkslategray',
    fill_color=headerColor,
    align=['left','center'],
    font=dict(color='white', size=12)
  ),
  cells=dict(
    values=[
      Active_per_country['Country/Region'],
      Active_per_country['Active_case'],
      ],
    line_color='darkslategray',
    # 2-D list of colors for alternating rows
    fill_color = [[rowOddColor,rowEvenColor,rowOddColor, rowEvenColor,rowOddColor]*len(Data_per_country)],
    align = ['left', 'center'],
    font = dict(color = 'darkslategray', size = 11)
    ))
])
fig.update_layout(
    title='Active Cases In Each Country',
)
fig.show()
In [30]:
fig = go.Figure(data=[go.Bar(
            x=Active_per_country['Country/Region'][0:10], y=Active_per_country['Active_case'][0:10],
            text=Active_per_country['Active_case'][0:10],
           
        )])
fig.update_layout(
    title='Most 10 infected Countries',
    xaxis_title="Countries",
    yaxis_title="Active Cases",
    template='plotly_white'
)
fig.show()

Deaths cases in each Country¶

In [31]:
Deaths_per_country = covid_data.groupby(["Country/Region"])["Deaths"].sum().reset_index().sort_values("Deaths",ascending=False).reset_index(drop=True)
In [32]:
headerColor = 'grey'
rowEvenColor = 'lightgrey'
rowOddColor = 'white'

fig = go.Figure(data=[go.Table(
  header=dict(
    values=['<b>Country</b>','<b>Deaths</b>'],
    line_color='darkslategray',
    fill_color=headerColor,
    align=['left','center'],
    font=dict(color='white', size=12)
  ),
  cells=dict(
    values=[
      Deaths_per_country['Country/Region'],
      Deaths_per_country['Deaths'],
      ],
    line_color='darkslategray',
    # 2-D list of colors for alternating rows
    fill_color = [[rowOddColor,rowEvenColor,rowOddColor, rowEvenColor,rowOddColor]*len(Data_per_country)],
    align = ['left', 'center'],
    font = dict(color = 'darkslategray', size = 11)
    ))
])
fig.update_layout(
    title='Deaths In Each Country',
)
fig.show()
In [33]:
fig = go.Figure(data=[go.Bar(
            x=Deaths_per_country['Country/Region'][0:10], y=Deaths_per_country['Deaths'][0:10],
            text=Deaths_per_country['Deaths'][0:10],
            textposition='auto',
            marker_color=' green'

        )])
fig.update_layout(
    title='Most 10 infected Countries',
    xaxis_title="Countries",
    yaxis_title="Deaths",
        template='plotly_white'

)
fig.show()

Predictions¶

In [34]:
base_stats_inc_df = pd.DataFrame(columns=['Index', 'Dates', 'Confirmed', 'Deaths', 'Recovered', 'Active', 'Daily Inc.'])
base_stats_inc_df[['Index', 'Dates', 'Confirmed', 'Deaths', 'Recovered', 'Active']] = base_stats[['index', 'Dates', 'Confirmed', 'Deaths', 'Recovered', 'Active']]
base_stats_inc_df['Daily Inc.'] = base_stats['index'].apply(lambda x: base_stats['Confirmed'][x]-base_stats['Confirmed'][x-1:x].sum())
In [35]:
days = np.array(base_stats_inc_df[['Index']]).reshape(-1, 1)
days_ex = []
for i in range(len(days)+30):
    days_ex = days_ex+[[i]]
In [36]:
prediction_df = pd.DataFrame(columns=['Index', 'Confirmed Pred', 'Deaths Pred', 'Recovered Pred', 'Active Pred', 'Daily Inc. Pred'])
prediction_df['Index'] = list(flatten(days_ex))
In [37]:
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import PolynomialFeatures
from sklearn import linear_model
from sklearn.metrics import r2_score


for col in base_stats_inc_df.columns[2:]:

    count = np.array(base_stats_inc_df[[col]]).reshape(-1, 1)

    X_train_confirmed, X_test_confirmed, y_train_confirmed, y_test_confirmed = train_test_split(
                                                        days[50:], count[50:], 
                                                        test_size=0.05, shuffle=False)

    MAE, RSE, R2 = [], [], []
    for j in range(1,10):
        #creating the model
        poly = PolynomialFeatures(degree=j)
        train_x_poly = poly.fit_transform(X_train_confirmed)
        
        regr_poly = linear_model.LinearRegression()
        regr_poly.fit(train_x_poly, y_train_confirmed)
        
        y_pred_poly = regr_poly.predict(poly.fit_transform(X_test_confirmed))
        MAE.append(np.mean(np.absolute(y_pred_poly - y_test_confirmed)))
        RSE.append(np.mean((y_pred_poly - list(flatten(y_test_confirmed))) ** 2))
        R2.append(r2_score(y_pred_poly, list(flatten(y_test_confirmed))))
        
    deg = RSE.index(min(RSE))+1
    #print("best deg for column {} is {}".format(col, deg))

    poly = PolynomialFeatures(degree=deg)
    train_x_poly = poly.fit_transform(X_train_confirmed)

    regr_poly = linear_model.LinearRegression()
    regr_poly.fit(train_x_poly, y_train_confirmed)
    col_name = col+' Pred'
    prediction_df[col_name] = list(flatten(regr_poly.predict(poly.fit_transform(days_ex))))
In [38]:
prediction_fig = go.Figure()
pred_dict = {
  "Confirmed": ["#118ab2", 'Confirmed', 'Predicted Confirmed','#149ECC'],
  "Active": ["#ef476f", 'Deaths', 'Predicted Deaths', '#F47C98'],
  "Recovered": ["#06d6a0", 'Recovered', 'Predicted Recovered','#24F9C1'],
  "Deaths": ["#073b4c", 'Active', 'Predicted Active','#0C6583'],
  "Daily Inc.": ["black", 'Daily Inc.', 'Predicted Daily Inc.','grey']
    }

for z in base_stats_inc_df.columns[2:]:
    
    z_pred = z+' Pred'
    prediction_fig.add_trace(go.Scatter(x=list(flatten(days)), y=base_stats_inc_df[z],
                                       line=dict(color=pred_dict[z][0]), name = pred_dict[z][1],
                                       hovertemplate ='<br><b>Day number</b>: %{x}'+'<br><i>No.of cases </i>:'+'%{y}'))
    
    prediction_fig.add_trace(go.Scatter(x=list(flatten(days_ex))[50:], y=prediction_df[z_pred][50:],
                                       line=dict(dash="dash", color='black'), visible=False, name = pred_dict[z][2],
                                       hovertemplate ='<br><b>Day number</b>: %{x}'+'<br><i>Predicted no.of cases </i>:'+'%{y}'))

    
    
    
prediction_fig.update_layout(
    updatemenus=[
        dict(
        buttons=list(
            [dict(label = 'Confirmed',
                  method = 'update',
                  args = [{'visible': [True, True, False, False, False, False, False, False, False, False]},
                          {'title': 'Confirmed Cases',
                           'showlegend':True}]),
             dict(label = 'Deaths',
                  method = 'update',
                  args = [{'visible': [False, False, True, True, False, False, False, False, False, False]},
                          {'title': 'Deaths Cases',
                           'showlegend':True}]),
             dict(label = 'Recovered',
                  method = 'update',
                  args = [{'visible': [False, False, False, False, True, True, False, False, False, False]},
                          {'title': 'Recovered Cases',
                           'showlegend':True}]),
             dict(label = 'Active',
                  method = 'update',
                  args = [{'visible': [False, False, False, False, False, False, True, True, False, False]},
                          {'title': 'Active Cases',
                           'showlegend':True}]),
             dict(label = 'Daily Inc.',
                  method = 'update',
                  args = [{'visible': [False, False, False, False, False, False, False, False, True, True]},
                          {'title': 'Daily Inc. Cases',
                           'showlegend':True}]),
            ]),
             type = "buttons",
             direction="down",
#             pad={"r": 10, "t": 40},
             showactive=True,
#              x=1.01,
#              xanchor="left",
             y=1.1,
             yanchor="top"
        )
    ])


prediction_fig.update_xaxes(showticklabels=False)
prediction_fig.update_layout(
    #height=500, width=1100, 
    title_text="Prediction for Covid19 Cases", title_x=0.5, title_font_size=20,
                            legend=dict(orientation='h',yanchor='top',y=1.12,xanchor='right',x=1), paper_bgcolor="mintcream",
                            xaxis_title="Number of Days <br> (Click on the buttons at the left to see the predictions)", yaxis_title="Count")
prediction_fig.show()

Coronavirus in my country India ¶

In [39]:
Data_india = covid_data [(covid_data['Country/Region'] == 'India') ].reset_index(drop=True)
Data_india.head()
Out[39]:
SNo ObservationDate Province/State Country/Region Last Update Confirmed Deaths Recovered Active_case
0 447 01/30/2020 Unknown India 1/30/20 16:00 1 0 0 1
1 510 01/31/2020 Unknown India 1/31/2020 23:59 1 0 0 1
2 568 02/01/2020 Unknown India 1/31/2020 8:15 1 0 0 1
3 630 02/02/2020 Unknown India 2020-02-02T06:03:08 2 0 0 2
4 697 02/03/2020 Unknown India 2020-02-03T21:43:02 3 0 0 3
In [40]:
fig = go.Figure(go.Bar(
            x=Data_india['ObservationDate'],
            y=Data_india['Active_case'],
    marker_color='rgb(13,48,100)'
           ))
fig.update_layout(
    title='Active cases In Each Day',
    template='plotly_white',
     xaxis_title="Active cases",
    yaxis_title="Days",
)
fig.show()
In [41]:
fig = go.Figure(go.Bar(
            x=Data_india['ObservationDate'],
            y=Data_india['Active_case'],
    marker_color='rgb(13,48,100)'
           ))
fig.update_layout(
    title='Active cases In Each Day',
    template='plotly_white',
     xaxis_title="Active cases",
    yaxis_title="Days",
)
fig.show()
In [42]:
fig = go.Figure(go.Bar(
            x=Data_india['ObservationDate'],
            y=Data_india['Recovered'],
    marker_color='rgb(13,48,100)'
           ))
fig.update_layout(
    title='Recovered cases In Each Day',
    template='plotly_white',
     xaxis_title="Recovered cases",
    yaxis_title="Days",
)
fig.show()
In [43]:
fig = go.Figure(go.Bar(
            x=Data_india['ObservationDate'],
            y=Data_india['Deaths'],
    marker_color='rgb(13,48,100)'
           ))
fig.update_layout(
    title='Deaths In Each Day',
    template='plotly_white',
     xaxis_title="Deaths",
    yaxis_title="Days",
)
fig.show()

Get Last Update¶

In [44]:
Data_india_last = Data_india[Data_india['ObservationDate'] == max(Data_india['ObservationDate'])].reset_index()
Data_india_last
Out[44]:
index SNo ObservationDate Province/State Country/Region Last Update Confirmed Deaths Recovered Active_case
0 7632 191894 12/31/2020 Andaman and Nicobar Islands India 2021-04-02 15:13:53 4941 62 4820 59
1 7633 191895 12/31/2020 Andhra Pradesh India 2021-04-02 15:13:53 881948 7104 871588 3256
2 7634 191912 12/31/2020 Arunachal Pradesh India 2021-04-02 15:13:53 16711 56 16549 106
3 7635 191913 12/31/2020 Assam India 2021-04-02 15:13:53 216139 1043 211838 3258
4 7636 191935 12/31/2020 Bihar India 2021-04-02 15:13:53 251348 1393 245156 4799
5 7637 191969 12/31/2020 Chandigarh India 2021-04-02 15:13:53 19682 316 18967 399
6 7638 191976 12/31/2020 Chhattisgarh India 2021-04-02 15:13:53 278540 3350 263251 11939
7 7639 191995 12/31/2020 Dadra and Nagar Haveli and Daman and Diu India 2021-04-02 15:13:53 3375 2 3364 9
8 7640 191999 12/31/2020 Delhi India 2021-04-02 15:13:53 624795 10523 608434 5838
9 7641 192035 12/31/2020 Goa India 2021-04-02 15:13:53 50981 737 49313 931
10 7642 192051 12/31/2020 Gujarat India 2021-04-02 15:13:53 244258 4302 229977 9979
11 7643 192057 12/31/2020 Haryana India 2021-04-02 15:13:53 262054 2899 255356 3799
12 7644 192064 12/31/2020 Himachal Pradesh India 2021-04-02 15:13:53 55114 931 51387 2796
13 7645 192090 12/31/2020 Jammu and Kashmir India 2021-04-02 15:13:53 120744 1880 115830 3034
14 7646 192093 12/31/2020 Jharkhand India 2021-04-02 15:13:53 114873 1027 112206 1640
15 7647 192111 12/31/2020 Karnataka India 2021-04-02 15:13:53 918544 12081 894834 11629
16 7648 192114 12/31/2020 Kerala India 2021-04-02 15:13:53 755718 3042 687104 65572
17 7649 192139 12/31/2020 Ladakh India 2021-04-02 15:13:53 9447 127 9132 188
18 7650 192140 12/31/2020 Lakshadweep India 2021-04-02 15:13:53 0 0 0 0
19 7651 192160 12/31/2020 Madhya Pradesh India 2021-04-02 15:13:53 240947 3595 227965 9387
20 7652 192166 12/31/2020 Maharashtra India 2021-04-02 15:13:53 1928603 49463 1824934 54206
21 7653 192168 12/31/2020 Manipur India 2021-04-02 15:13:53 28137 354 26601 1182
22 7654 192181 12/31/2020 Meghalaya India 2021-04-02 15:13:53 13408 139 13085 184
23 7655 192195 12/31/2020 Mizoram India 2021-04-02 15:13:53 4204 8 4091 105
24 7656 192207 12/31/2020 Nagaland India 2021-04-02 15:13:53 11921 79 11624 218
25 7657 192251 12/31/2020 Odisha India 2021-04-02 15:13:53 329306 1871 325103 2332
26 7658 192285 12/31/2020 Puducherry India 2021-04-02 15:13:53 38096 633 37100 363
27 7659 192289 12/31/2020 Punjab India 2021-04-02 15:13:53 166239 5331 157043 3865
28 7660 192299 12/31/2020 Rajasthan India 2021-04-02 15:13:53 307554 2689 295030 9835
29 7661 192348 12/31/2020 Sikkim India 2021-04-02 15:13:53 5877 127 5218 532
30 7662 192369 12/31/2020 Tamil Nadu India 2021-04-02 15:13:53 817077 12109 796353 8615
31 7663 192373 12/31/2020 Telangana India 2021-04-02 15:13:53 286354 1541 278839 5974
32 7664 192390 12/31/2020 Tripura India 2021-04-02 15:13:53 33264 385 32751 128
33 7665 192405 12/31/2020 Unknown India 2021-04-02 15:13:53 0 0 0 0
34 7666 192416 12/31/2020 Uttar Pradesh India 2021-04-02 15:13:53 584966 8352 562459 14155
35 7667 192417 12/31/2020 Uttarakhand India 2021-04-02 15:13:53 90616 1504 84149 4963
36 7668 192444 12/31/2020 West Bengal India 2021-04-02 15:13:53 550893 9683 528829 12381
In [45]:
colors = ['rgb(2,58,88)','rgb(65,171,93)', 'rgb(127,0,0)']
labels = ["Active cases","Recovered","Deaths"]
values = Data_india_last.loc[0, ["Active_case","Recovered","Deaths"]]

fig = go.Figure(data=[go.Pie(labels=labels,
                             values=values)])
fig.update_traces(hoverinfo='label+percent', textinfo='value', textfont_size=20,
                  marker=dict(colors=colors, line=dict(color='#000000', width=2)))
fig.show()

**Coronavirus in China** ¶

In [46]:
Data_China = covid_data [(covid_data['Country/Region'] == 'China') ].reset_index(drop=True)
Data_China.head()
Out[46]:
SNo ObservationDate Province/State Country/Region Last Update Confirmed Deaths Recovered Active_case
0 1 01/22/2020 Anhui China 1/22/2020 17:00 1 0 0 1
1 2 01/22/2020 Beijing China 1/22/2020 17:00 14 0 0 14
2 3 01/22/2020 Chongqing China 1/22/2020 17:00 6 0 0 6
3 4 01/22/2020 Fujian China 1/22/2020 17:00 1 0 0 1
4 5 01/22/2020 Gansu China 1/22/2020 17:00 0 0 0 0

Get last update in china¶

In [47]:
Data_china_last = Data_China[Data_China['ObservationDate'] == max(Data_China['ObservationDate'])].reset_index()
Data_china_last.head()
Out[47]:
index SNo ObservationDate Province/State Country/Region Last Update Confirmed Deaths Recovered Active_case
0 11006 191897 12/31/2020 Anhui China 2021-04-02 15:13:53 993 6 986 1
1 11007 191931 12/31/2020 Beijing China 2021-04-02 15:13:53 987 9 944 34
2 11008 191981 12/31/2020 Chongqing China 2021-04-02 15:13:53 590 6 584 0
3 11009 192023 12/31/2020 Fujian China 2021-04-02 15:13:53 513 1 488 24
4 11010 192028 12/31/2020 Gansu China 2021-04-02 15:13:53 182 2 180 0

Confirmed cases in every Province/State in china¶

In [48]:
Data_china_per_state= Data_china_last.groupby(["Province/State"])["Confirmed","Active_case","Recovered","Deaths"].sum().reset_index().sort_values("Confirmed",ascending=False).reset_index(drop=True)
In [49]:
fig = px.pie(Data_china_per_state, values=Data_china_per_state['Confirmed'], names=Data_china_per_state['Province/State'],
             title='Confirmed cases in China',
            hole=.2)
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.show()
In [50]:
fig = go.Figure(go.Bar(
            x=Data_china_per_state['Active_case'],
            y=Data_china_per_state['Province/State'],
            orientation='h',
            marker_color='#DC3912',))
fig.update_layout(
    title='Active Cases In Each Province/State',
    template='plotly_white',
    xaxis_title="Active Cases",
    yaxis_title="Province/State",
)
fig.show()
In [51]:
fig = go.Figure(go.Bar(
            x=Data_china_per_state['Recovered'],
            y=Data_china_per_state['Province/State'],
            orientation='h',
            marker_color='green',))
fig.update_layout(
    title='Active Cases In Each Province/State',
    template='plotly_white',
    xaxis_title="Recovered Cases",
    yaxis_title="Province/State",
)
fig.show()
In [52]:
fig = go.Figure(go.Bar(
            x=Data_china_per_state['Deaths'],
            y=Data_china_per_state['Province/State'],
            orientation='h',
            marker_color='black',))
fig.update_layout(
    title='Deaths In Each Province/State',
    template='plotly_white',
    xaxis_title="Deaths",
    yaxis_title="Province/State",
)
fig.show()

Get total cases in China¶

In [53]:
Data_china_total= Data_china_last.groupby(["Country/Region"])["Confirmed","Deaths","Recovered","Active_case"].sum().reset_index().reset_index(drop=True)
In [54]:
labels = ["Active cases","Recovered","Deaths"]
values = Data_china_total.loc[0, ["Active_case","Recovered","Deaths"]]
df = px.data.tips()
fig = px.pie(Data_china_total, values=values, names=labels, color_discrete_sequence=['green','royalblue','darkblue'], hole=0.5)
fig.update_layout(
    title='Total cases in China : '+str(Data_china_total["Confirmed"][0]),
)
fig.show()

**Coronavirus in United States** ¶

In [55]:
Data_US = covid_data [(covid_data['Country/Region'] == 'US') ].reset_index(drop=True)

Get last update in US¶

In [56]:
Data_us_last = Data_US[Data_US['ObservationDate'] == max(Data_US['ObservationDate'])].reset_index()
In [57]:
Data_us_total= Data_us_last.groupby(["Country/Region"])["Confirmed","Deaths","Recovered","Active_case"].sum().reset_index().reset_index(drop=True)
In [58]:
labels = ["Active cases","Recovered","Deaths"]
values = Data_us_total.loc[0, ["Active_case","Recovered","Deaths"]]
df = px.data.tips()
fig = px.pie(Data_us_total, values=values, names=labels, color_discrete_sequence=['royalblue','darkblue','green'], hole=0.5)
fig.update_layout(
    title='Total cases in United States : '+str(Data_us_total["Confirmed"][0]),
)
fig.show()

Cases in every Province/State in US¶

In [59]:
Data_us_per_state= Data_us_last.groupby(["Province/State"])["Confirmed","Active_case","Deaths"].sum().reset_index().sort_values("Confirmed",ascending=False).reset_index(drop=True)
In [60]:
fig = px.pie(Data_us_per_state, values=Data_us_per_state['Confirmed'], names=Data_us_per_state['Province/State'],
             title='Confirmed cases in United States',
            hole=.2)
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.show()
In [61]:
fig = px.pie(Data_us_per_state, values=Data_us_per_state['Active_case'], names=Data_us_per_state['Province/State'],
             title='Active cases in United States',
            hole=.2)
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.show()
In [62]:
fig = px.pie(Data_us_per_state, values=Data_us_per_state['Deaths'], names=Data_us_per_state['Province/State'],
             title='Deaths in United States',
            hole=.2)
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.show()

**Coronavirus in Spain** ¶

In [63]:
Data_Spain = covid_data [(covid_data['Country/Region'] == 'Spain') ].reset_index(drop=True)
In [64]:
Data_spain = Data_Spain[Data_Spain['ObservationDate'] == max(Data_Spain['ObservationDate'])].reset_index()
Data_spain_last= Data_spain.groupby(["Country/Region"])["Confirmed","Deaths","Recovered","Active_case"].sum().reset_index().reset_index(drop=True)
In [65]:
labels = ["Active cases","Recovered","Deaths"]
values = Data_spain_last.loc[0, ["Active_case","Recovered","Deaths"]]
df = px.data.tips()
fig = px.pie(Data_spain_last, values=values, names=labels, color_discrete_sequence=['royalblue','green','darkblue'], hole=0.5)
fig.update_layout(
    title='Total cases in Spain : '+str(Data_spain_last["Confirmed"][0]),
)
fig.show()
In [66]:
Data_spain_per_state= Data_spain.groupby(["Province/State"])["Confirmed","Deaths","Recovered","Active_case"].sum().reset_index().sort_values("Confirmed",ascending=False).reset_index(drop=True)
In [67]:
fig = px.treemap(Data_spain_per_state, path=['Province/State'], values=Data_spain_per_state['Confirmed'], height=700,
                 title='Confirmed cases in Spain', color_discrete_sequence = px.colors.qualitative.Dark2)
fig.data[0].textinfo = 'label+text+value'
fig.show()
In [68]:
fig = px.treemap(Data_spain_per_state, path=['Province/State'], values=Data_spain_per_state['Recovered'], height=700,
                 title='Recovered cases in Spain', color_discrete_sequence = px.colors.qualitative.Dark2)
fig.data[0].textinfo = 'label+text+value'
fig.show()
In [69]:
fig = px.treemap(Data_spain_per_state, path=['Province/State'], values=Data_spain_per_state['Active_case'], height=700,
                 title='Active cases in Spain', color_discrete_sequence = px.colors.sequential.deep)
fig.data[0].textinfo = 'label+text+value'
fig.show()
In [70]:
fig = px.treemap(Data_spain_per_state, path=['Province/State'], values=Data_spain_per_state['Deaths'], height=700,
                 title='Deaths in Spain', color_discrete_sequence = px.colors.sequential.deep)
fig.data[0].textinfo = 'label+text+value'
fig.show()

**US X The rest of the word** ¶

In [71]:
Data_US_op= Data_US.groupby(["ObservationDate","Country/Region"])["Confirmed","Deaths","Recovered","Active_case"].sum().reset_index().reset_index(drop=True)
In [72]:
Data_Word = covid_data [(covid_data['Country/Region'] != 'US') ].reset_index(drop=True)
Data_WORD_last = Data_Word[Data_Word['ObservationDate'] == max(Data_Word['ObservationDate'])].reset_index()
Data_us_total= Data_us_last.groupby(["Country/Region"])["Confirmed","Deaths","Recovered","Active_case"].sum().reset_index().reset_index(drop=True)
Data_word_total= Data_WORD_last.groupby(["ObservationDate"])["Confirmed","Deaths","Recovered","Active_case"].sum().reset_index().reset_index(drop=True)

Data_Word_op= Data_Word.groupby(["ObservationDate"])["Confirmed","Deaths","Recovered","Active_case"].sum().reset_index().reset_index(drop=True)
In [73]:
fig = go.Figure()
fig.add_trace(go.Scatter(x=Data_US_op.index, y=Data_US_op['Confirmed'],
                    mode='lines',
                    name='Confirmed cases in US'))


fig.add_trace(go.Scatter(x=Data_Word_op.index, y=Data_Word_op['Confirmed'],
                    mode='lines',
                    name='Confirmed cases in The Rest Of The Word'))

fig.update_layout(
    title='Evolution of Confirmed cases over time in US and The Rest Of The Word',
        template='plotly_white'

)

fig.show()
In [74]:
fig = go.Figure()
fig.add_trace(go.Scatter(x=Data_US_op.index, y=Data_US_op['Active_case'],
                    mode='lines',
                    name='Active cases in US'))


fig.add_trace(go.Scatter(x=Data_Word_op.index, y=Data_Word_op['Active_case'],
                    mode='lines',
                    name='Active cases in The Rest Of The Word'))

fig.update_layout(
    title='Evolution of Active cases over time in US and The Rest Of The Word',
        template='plotly_dark'

)

fig.show()
In [75]:
fig = go.Figure()
fig.add_trace(go.Scatter(x=Data_US_op.index, y=Data_US_op['Recovered'],
                    mode='lines',
                    name='Recovered cases in US'))


fig.add_trace(go.Scatter(x=Data_Word_op.index, y=Data_Word_op['Recovered'],
                    mode='lines',
                    name='Recovered cases in The Rest Of The Word'))

fig.update_layout(
    title='Evolution of Recovered cases over time in US and The Rest Of The Word',
        template='plotly_dark'

)

fig.show()
In [76]:
fig = go.Figure()
fig.add_trace(go.Scatter(x=Data_US_op.index, y=Data_US_op['Deaths'],
                    mode='lines',
                    name='Deaths in US'))


fig.add_trace(go.Scatter(x=Data_Word_op.index, y=Data_Word_op['Deaths'],
                    mode='lines',
                    name='Deathsin The Rest Of The Word'))
fig.update_layout(
    title='Evolution of Deaths over time in US and The Rest Of The Word',
        template='plotly_white'

)

fig.show()
In [ ]: